A Study on the Risk Assessment for Urban Railway Systems Using an Adaptive Neuro-Fuzzy Inference System(ANFIS) 


Vol. 37,  No. 1, pp. 78-87, Feb.  2022
10.14346/JKOSOS.2022.37.1.78


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  Abstract

In the risk assessment of urban railway systems, a hazard log is created by identifying hazards from accident and failure data. Then, based on a risk matrix, evaluators analyze the frequency and severity of the occurrence of the hazards, conduct the risk assessment, and then establish safety measures for the risk factors prior to risk control. However, because subjective judgments based on the evaluators' experiences affect the risk assessment results, a more objective and automated risk assessment system must be established. In this study, we propose a risk assessment model in which an adaptive neuro-fuzzy inference system (ANFIS), which is combined in artificial neural networks (ANN) and fuzzy inference system (FIS), is applied to the risk assessment of urban railway systems. The newly proposed model is more objective and automated, alleviating the limitations of risk assessments that use a risk matrix. In addition, the reliability of the model was verified by comparing the risk assessment results and risk control priorities between the newly proposed ANFIS-based risk assessment model and the risk assessment using a risk matrix. Results of the comparison indicate that a high level of accuracy was demonstrated in the risk assessment results of the proposed model, and uncertainty and subjectivity were mitigated in the risk control priority.

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  Cite this article

[IEEE Style]

탁길훈 and 구정서, "A Study on the Risk Assessment for Urban Railway Systems Using an Adaptive Neuro-Fuzzy Inference System(ANFIS)," Journal of the Korean Society of Safety, vol. 37, no. 1, pp. 78-87, 2022. DOI: 10.14346/JKOSOS.2022.37.1.78.

[ACM Style]

탁길훈 and 구정서. 2022. A Study on the Risk Assessment for Urban Railway Systems Using an Adaptive Neuro-Fuzzy Inference System(ANFIS). Journal of the Korean Society of Safety, 37, 1, (2022), 78-87. DOI: 10.14346/JKOSOS.2022.37.1.78.

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